What Is Retrieval-Augmented Generation (RAG)?

Retrieval-augmented generation (RAG) is a technique that retrieves relevant information from a data source and gives it to a language model as context before the model answers. This lets the model respond from real, current, specific data rather than from its training alone, which improves accuracy and lets answers cite their sources.

How RAG works

RAG adds a retrieval step in front of the model. Source documents are prepared ahead of time: split into passages, converted into embeddings, and stored in a vector database. When a question comes in, the system searches that store for the passages most relevant to the question, then places those passages into the model's prompt alongside the question. The model generates its answer using that supplied context.

The result is an answer grounded in specific retrieved material rather than the model's general memory. Because the knowledge lives in the data source, it can be updated by changing the documents, with no need to retrain the model, and the retrieved passages can be shown as citations so a reader can check where the answer came from.

Why RAG matters for AI

RAG matters because it addresses two of the biggest weaknesses of language models: they make things up, and their knowledge is frozen at a training cutoff. By grounding answers in retrieved sources, RAG cuts hallucination and lets a model draw on current and proprietary information it never trained on. It is also cheaper and easier to keep current than fine-tuning, since you update data rather than retrain. This is why RAG is one of the most common patterns for putting language models to work on a company's own knowledge. At Custom AI Studio, RAG is central to how we build systems that answer accurately from a client's own documents and data.